Deployment

Table of Contents Introduction Why Inference Optimization Matters Fundamentals of Quantization 3.1 Floating‑Point vs Fixed‑Point Representations 3.2 Common Quantization Schemes 3.3 Quantization‑Aware Training vs Post‑Training Quantization Practical Quantization Workflows for LLMs 4.1 Using 🤗 Transformers + BitsAndBytes 4.2 GPTQ & AWQ: Fast Approximate Quantization 4.3 Exporting to ONNX & TensorRT Benchmarking Quantized Models 5.1 Latency, Throughput, and Memory Footprint 5.2 Accuracy Trade‑offs: Perplexity & Task‑Specific Metrics Introducing vLLM: High‑Performance LLM Serving 6.1 Core Architecture and Scheduler 6.2 GPU Memory Management & Paging Deploying Quantized Models with vLLM 7.1 Installation & Environment Setup 7.2 Running a Quantized Model (Example: LLaMA‑7B‑4bit) 7.3 Scaling Across Multiple GPUs & Nodes Advanced Strategies: Mixed‑Precision, KV‑Cache Compression, and Async I/O Real‑World Case Studies 9.1 Customer Support Chatbot at a FinTech Startup 9.2 Semantic Search over Billion‑Document Corpus Best Practices & Common Pitfalls 11 Conclusion 12 Resources Introduction Large Language Models (LLMs) have transitioned from research curiosities to production‑grade engines powering chat assistants, code generators, and semantic search systems. Yet, the sheer size of state‑of‑the‑art models—often exceeding dozens of billions of parameters—poses a practical challenge: inference cost. ...

FastAPI’s speed, async capabilities, and automatic API documentation make it ideal for building production-grade APIs serving Large Language Models (LLMs). This guide details best practices for deploying scalable, secure FastAPI applications handling LLM inference, streaming responses, and high-throughput requests.[1][3][5] LLM APIs often face unique challenges: high memory usage, long inference times, streaming outputs, and massive payloads. We’ll cover project structure, async optimization, security, deployment, and LLM-specific patterns like token streaming and caching. ...

Large Language Model (LLM) sandboxes are isolated, secure environments designed to run powerful AI models while protecting user data, preventing unauthorized access, and mitigating risks like code execution vulnerabilities. These setups enable safe experimentation, research, and deployment of LLMs in institutional or enterprise settings.[1][2][3] What is an LLM Sandbox? An LLM sandbox creates a controlled “playground” for interacting with LLMs, shielding sensitive data from external providers and reducing security risks. Unlike direct API calls to cloud services like OpenAI, sandboxes often host models locally or in managed cloud instances, ensuring inputs aren’t used for training vendor models.[2] ...